Weiming Liu
ABSTRACT
Cross-Domain Recommendation (CDR) has been popularly studied for solving the data sparsity problem via leveraging rich knowledge from the auxiliary domain. Most of the current CDR models assume that user-item ratings/reviews are both accessible during the training procedure. However, it is impractical nowadays due to the strict data privacy protection policy. In this paper, we focus on the Privacy-Preserving Cross-Domain Recommendation problem (PPCDR). Although some previous approaches have investigated the problems, they always fail to effectively utilize the rating and review for user preference modeling. What is worse, they separate the processes of privacy data modeling and user-item collaborative filtering, leading to suboptimal solutions. To fill this gap, we propose the Differentially Private Sparse Mapping Recommendation model (DPSMRec), an end-to-end cross-domain recommendation framework for solving the PPCDR problem. DPSMRec includes three main modules, i.e., source embedding generation module, target rating prediction module, and differentially private sparse mapping module. Specifically, the source embedding generation module and target rating prediction module are set to exploit the ratings and review in each domain. The differentially private sparse mapping module aims to transfer knowledge among the overlapped users via Wasserstein distance privately. To obtain more accurate mapping solutions, we further propose differentially private enhanced sparse optimal transport via fused Gromov-Wasserstein distance which can consider both structure and semantic information among the users across domains. Our empirical study on Amazon and Douban datasets demonstrates that DPSMRec significantly outperforms the state-of-the-art models under the PPCDR setting.
- Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 308--318.Google Scholar
Digital Library
- Tamara Abdulmunim Abduljabbar, Xiaohui Tao, Ji Zhang, Xujuan Zhou, Lin Li, and Yi Cai. 2021. A survey of privacy solutions using blockchain for recommender systems: Current status, classification and open issues. Comput. J. 64, 7 (2021), 1104--1129.Google Scholar
- Luigi Ambrosio, Klaus Deckelnick, Gerhard Dziuk, Masayasu Mimura, Vsevolod A Solonnikov, Halil Mete Soner, and Luigi Ambrosio. 2003. Lecture notes on optimal transport problems. Springer.Google Scholar
- Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet. 2012. The johnsonlindenstrauss transform itself preserves differential privacy. In 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science. IEEE, 410--419.Google ScholarDigital Library
- Mathieu Blondel, Vivien Seguy, and Antoine Rolet. 2018. Smooth and sparse optimal transport. In International conference on artificial intelligence and statistics. PMLR, 880--889.Google Scholar
- Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. arXiv preprint arXiv:2302.08191 (2023).Google Scholar
- Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, and Bin Wang. 2022. Cross- Domain Recommendation to Cold-Start Users via Variational Information Bottleneck. In ICDE.Google Scholar
- Di Chai, Leye Wang, Kai Chen, and Qiang Yang. 2020. Secure federated matrix factorization. IEEE Intelligent Systems 36, 5 (2020), 11--20.Google ScholarCross Ref
- Kamalika Chaudhuri and Claire Monteleoni. 2008. Privacy-preserving logistic regression. Advances in neural information processing systems 21 (2008).Google Scholar
- Chaochao Chen and et al. 2022. Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation. In WWW 2022. 1455--1465.Google Scholar
- Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, and Xiaolong Li. 2018. Privacy preserving point-of-interest recommendation using decentralized matrix factorization. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarCross Ref
- Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural attentional rating regression with review-level explanations. In WWW. 1583--1592.Google Scholar
- Gaode Chen, Xinghua Zhang, Yijun Su, Yantong Lai, Ji Xiang, Junbo Zhang, and Yu Zheng. 2023. Win-Win: A Privacy-Preserving Federated Framework for Dual- Target Cross-Domain Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 4149--4156.Google ScholarDigital Library
- Xu Chen, Ya Zhang, Ivor Tsang, Yuangang Pan, and Jingchao Su. 2020. Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation. arXiv preprint arXiv:2009.06884 (2020).Google Scholar
- Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy. 2017. Joint distribution optimal transportation for domain adaptation. Advances in neural information processing systems 30 (2017).Google Scholar
- Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, and Guoping Hu. 2019. Pre-training with whole word masking for chinese bert. arXiv preprint arXiv:1906.08101 (2019).Google Scholar
- Marco Cuturi. 2013. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 (2013), 2292--2300.Google Scholar
- Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty. 2018. Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation. In ECCV. 447--463.Google Scholar
- Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography: Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4-7, 2006. Proceedings 3. Springer, 265--284.Google ScholarDigital Library
- Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, et al. 2021. Pot: Python optimal transport. The Journal of Machine Learning Research 22, 1 (2021), 3571--3578.Google ScholarDigital Library
- R Flamary, N Courty, D Tuia, and A Rakotomamonjy. 2016. Optimal transport for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell 1 (2016).Google Scholar
- Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2021. DA-GCN: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. arXiv preprint arXiv:2105.03300 (2021).Google Scholar
- Deren Han and Xiaoming Yuan. 2012. A note on the alternating direction method of multipliers. Journal of Optimization Theory and Applications 155 (2012), 227--238.Google ScholarDigital Library
- Moritz Hardt and Aaron Roth. 2013. Beyond worst-case analysis in private singular vector computation. In Proceedings of the forty-fifth annual ACM symposium on Theory of computing. 331--340.Google ScholarDigital Library
- Ruining He and Julian McAuley. 2016. VBPR: visual bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI conference on artificial intelligence, Vol. 30.Google ScholarCross Ref
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.Google ScholarDigital Library
- Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. Conet: Collaborative cross networks for cross-domain recommendation. In Proceedings of the 27th ACM international conference on information and knowledge management. 667--676.Google ScholarDigital Library
- Jingyu Hua, Chang Xia, and Sheng Zhong. 2015. Differentially Private Matrix Factorization. In Proceedings of the 24th International Conference on Artificial Intelligence (Buenos Aires, Argentina) (IJCAI'15). AAAI Press, 1763--1770.Google ScholarDigital Library
- Weiming Huang, Baisong Liu, and Hao Tang. 2019. Privacy protection for recommendation system: a survey. In Journal of Physics: Conference Series, Vol. 1325. IOP Publishing, 012087.Google Scholar
- William B Johnson, Joram Lindenstrauss, and Gideon Schechtman. 1986. Extensions of Lipschitz maps into Banach spaces. Israel Journal of Mathematics 54, 2 (1986), 129--138.Google ScholarCross Ref
- Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra. 2012. Privacy via the johnson-lindenstrauss transform. arXiv preprint arXiv:1204.2606 (2012).Google Scholar
- D. Kingma and J. Ba. 2014. Adam: A Method for Stochastic Optimization. Computer Science (2014).Google Scholar
- Khang Le, Dung Q Le, Huy Nguyen, Dat Do, Tung Pham, and Nhat Ho. 2022. Entropic Gromov-Wasserstein between Gaussian Distributions. In International Conference on Machine Learning. PMLR, 12164--12203.Google Scholar
- Khang Le, Huy Nguyen, Quang Nguyen, Nhat Ho, Tung Pham, and Hung Bui. 2021. On robust optimal transport: Computational complexity, low-rank approximation, and barycenter computation. arXiv preprint arXiv:2102.06857 (2021).Google Scholar
- Nam Lê Tien, Amaury Habrard, and Marc Sebban. 2019. Differentially Private Optimal Transport: Application to Domain Adaptation.. In IJCAI. 2852--2858.Google Scholar
- B. Li, Y. Qiang, and X. Xue. 2009. Can movies and books collaborate?: crossdomain collaborative filtering for sparsity reduction. Sun Yat-sen University 38, 4 (2009), 2052--2057.Google Scholar
- Bin Li, Qiang Yang, and Xiangyang Xue. 2009. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th annual international conference on machine learning. 617--624.Google ScholarDigital Library
- Pan Li and Alexander Tuzhilin. 2020. Ddtcdr: Deep dual transfer cross domain recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 331--339.Google ScholarDigital Library
- Xinhang Li, Zhaopeng Qiu, Xiangyu Zhao, Zihao Wang, Yong Zhang, Chunxiao Xing, and Xian Wu. 2022. Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1199--1208.Google ScholarDigital Library
- Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, and Mori Kurokawa. 2022. Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations. ArXiv abs/2211.03390 (2022).Google Scholar
- Qiang Liu, Shu Wu, and Liang Wang. 2017. Deepstyle: Learning user preferences for visual recommendation. In Proceedings of the 40th international acm sigir conference on research and development in information retrieval. 841--844.Google ScholarDigital Library
- Weiming Liu and et al. 2022. Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation. In SIGIR. 312--321.Google Scholar
- Weiming Liu and et.al. 2023. Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation. In IJCAI 2023.Google Scholar
- Weiming Liu, Jiajie Su, Chaochao Chen, and Xiaolin Zheng. 2021. Leveraging distribution alignment via stein path for cross-domain cold-start recommendation. Advances in Neural Information Processing Systems 34 (2021), 19223--19234.Google Scholar
- Weiming Liu, Xiaolin Zheng, Chaochao Chen, Jiajie Su, Xinting Liao, Mengling Hu, and Yanchao Tan. 2023. Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation. In Proceedings of the ACM Web Conference 2023. 383--394.Google ScholarDigital Library
- Weiming Liu, Xiaolin Zheng, Mengling Hu, and Chaochao Chen. 2022. Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation. arXiv preprint arXiv:2202.04920 (2022).Google Scholar
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).Google Scholar
- Frank McSherry and Ilya Mironov. 2009. Differentially private recommender systems: Building privacy into the netflix prize contenders. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. 627--636.Google ScholarDigital Library
- Wu Meihan, Li Li, Chang Tao, Eric Rigall,Wang Xiaodong, and Xu Cheng-Zhong. 2022. FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction. In CIKM. 2179--2188.Google Scholar
- Derek Miller. 2019. Leveraging BERT for extractive text summarization on lectures. arXiv preprint arXiv:1906.04165 (2019).Google Scholar
- Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). 188--197.Google ScholarCross Ref
- Taiwo Blessing Ogunseyi, Cossi Blaise Avoussoukpo, and Yiqiang Jiang. 2021. Privacy-preserving matrix factorization for cross-domain recommendation. IEEE Access 9 (2021), 91027--91037.Google ScholarCross Ref
- Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar. 2016. Semi-supervised knowledge transfer for deep learning from private training data. arXiv preprint arXiv:1610.05755 (2016).Google Scholar
- Dhanya Pramod. 2023. Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda. Data Technologies and Applications 57, 1 (2023), 32--55.Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).Google Scholar
- Jiajie Su, Chaochao Chen,Weiming Liu, FeiWu, Xiaolin Zheng, and Haoming Lyu. 2023. Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation. In Proceedings of the ACM Web Conference 2023. 165--176.Google ScholarDigital Library
- Vayer Titouan, Nicolas Courty, Romain Tavenard, and Rémi Flamary. 2019. Optimal transport for structured data with application on graphs. In International Conference on Machine Learning. PMLR, 6275--6284.Google Scholar
- Santosh S Vempala. 2005. The random projection method. Vol. 65. American Mathematical Soc.Google Scholar
- Cédric Villani et al. 2009. Optimal transport: old and new. Vol. 338. Springer.Google Scholar
- Cheng Wang, Mathias Niepert, and Hui Li. 2019. Recsys-dan: discriminative adversarial networks for cross-domain recommender systems. IEEE transactions on neural networks and learning systems 31, 8 (2019), 2731--2740.Google Scholar
- Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 950--958.Google ScholarDigital Library
- Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, and Lawrence Carin. 2020. Learning autoencoders with relational regularization. In International Conference on Machine Learning. PMLR, 10576--10586.Google Scholar
- Dengcheng Yan, Yuchuan Zhao, Zhongxiu Yang, Ying Jin, and Yiwen Zhang. 2022. FedCDR: Privacy-preserving federated cross-domain recommendation. Digital Communications and Networks 8, 4 (2022), 552--560.Google ScholarCross Ref
- Wenhui Yu, Xiao Lin, Junfeng Ge, Wenwu Ou, and Zheng Qin. 2020. Semisupervised collaborative filtering by text-enhanced domain adaptation. In KDD. 2136--2144.Google Scholar
- Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, and Jiadi Yu. 2021. A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions. Proceedings of the ACM on Measurement and Analysis of Computing Systems (2021).Google Scholar
- Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, and Jiadi Yu. 2022. A survey on cross-domain recommendation: taxonomies, methods, and future directions. ACM Transactions on Information Systems 41, 2 (2022), 1--39.Google ScholarDigital Library
- Changqing Zhang, Yu Geng, Zongbo Han, Yeqing Liu, Huazhu Fu, and Qinghua Hu. 2022. Autoencoder in Autoencoder Networks. IEEE transactions on neural networks and learning systems (2022).Google ScholarCross Ref
- Changqing Zhang, Yeqing Liu, and Huazhu Fu. 2019. Ae2-nets: Autoencoder in autoencoder networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2577--2585.Google ScholarCross Ref
- Shuai Zhang, Lina Yao, Aixin Sun, SenWang, Guodong Long, and Manqing Dong. 2018. Neurec: On nonlinear transformation for personalized ranking. arXiv preprint arXiv:1805.03002 (2018).Google ScholarDigital Library
- Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, and Aixin Sun. 2020. CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network. 229--238. https://doi.org/10.1145/3397271.3401169Google ScholarDigital Library
- Chuang Zhao, Hongke Zhao, Ming He, Jian Zhang, and Jianping Fan. 2023. Crossdomain recommendation via user interest alignment. In Proceedings of the ACM Web Conference 2023. 887--896.Google ScholarDigital Library
- Yi Zhao, Chaozhuo Li, Jiquan Peng, Xiaohan Fang, Feiran Huang, SenzhangWang, Xing Xie, and Jibing Gong. 2023. Beyond the Overlapping Users: Cross-Domain Recommendation via Adaptive Anchor Link Learning. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1488--1497.Google ScholarDigital Library
- Lei Zheng, Vahid Noroozi, and Philip S Yu. 2017. Joint deep modeling of users and items using reviews for recommendation. In WSDM. 425--434.Google Scholar
- Xiaolin Zheng, Jiajie Su, Weiming Liu, and Chaochao Chen. 2022. DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation. Proceedings of the 30th ACM International Conference on Multimedia (2022).Google ScholarDigital Library
- Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019. DTCDR: A framework for dual-target cross-domain recommendation. In CIKM. 1533--1542.Google ScholarDigital Library
- Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, and Guanfeng Liu. 2021. A unified framework for cross-domain and cross-system recommendations. IEEE Transactions on Knowledge and Data Engineering (2021).Google ScholarCross Ref
Index Terms
- Differentially Private Sparse Mapping for Privacy-Preserving Cross Domain Recommendation
Recommendations
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
WWW '22: Proceedings of the ACM Web Conference 2022Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-start and data sparsity problem commonly existed in recommender systems. CDR models can improve the recommendation performance of a target domain by leveraging the data ...
Privacy-Preserving Cross-Domain Recommendation with Federated Graph Learning
As people inevitably interact with items across multiple domains or various platforms, cross-domain recommendation (CDR) has gained increasing attention. However, the rising privacy concerns limit the practical applications of existing CDR models since ...
Domain ranking for cross domain collaborative filtering
UMAP'12: Proceedings of the 20th international conference on User Modeling, Adaptation, and PersonalizationIn recommendation systems a variation of the cold start problem is a situation where the target user has few-to-none item ratings belonging to the target domain (e.g., movies) to base recommendations on. One way to overcome this is by basing ...
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